Observations-Marshmallow Experiment

The marshmallow experiment is a famous experiment, it has been referred to by people studying cognition, motivation, and decision making because the experiment demonstrates that a person’s ability to delay gratification is a trait that correlate with success in the later life. In other words, the children who are able to put off the gratification stage are more likely to have success in their lives. Here is an excerpt from James Clears web page that describes the experiment.

In the 1960s, a Stanford professor named Walter Mischel began conducting a series of important psychological studies.

During his experiments, Mischel and his team tested hundreds of children — most of them around the ages of 4 and 5 years old — and revealed what is now believed to be one of the most important characteristics for success in health, work, and life.

The Marshmallow Experiment

The experiment began by bringing each child into a private room, sitting them down in a chair, and placing a marshmallow on the table in front of them.

At this point, the researcher offered a deal to the child.

The researcher told the child that he was going to leave the room and that if the child did not eat the marshmallow while he was away, then they would be rewarded with a second marshmallow. However, if the child decided to eat the first one before the researcher came back, then they would not get a second marshmallow.

So the choice was simple: one treat right now or two treats later.

The researcher left the room for 15 minutes.

As you can imagine, the footage of the children waiting alone in the room was rather entertaining. Some kids jumped up and ate the first marshmallow as soon as the researcher closed the door. Others wiggled and bounced and scooted in their chairs as they tried to restrain themselves, but eventually gave in to temptation a few minutes later. And finally, a few of the children did manage to wait the entire time.

Published in 1972, this popular study became known as The Marshmallow Experiment, but it wasn't the treat that made it famous. The interesting part came years later.

The Power of Delayed Gratification

As the years rolled on and the children grew up, the researchers conducted follow up studies and tracked each child's progress in a number of areas. What they found was surprising.

The children who were willing to delay gratification and waited to receive the second marshmallow ended up having higher SAT scores, lower levels of substance abuse, lower likelihood of obesity, better responses to stress, better social skills as reported by their parents, and generally better scores in a range of other life measures.

The researchers followed each child for more than 40 years and over and over again, the group who waited patiently for the second marshmallow succeed in whatever capacity they were measuring. In other words, this series of experiments proved that the ability to delay gratification was critical for success in life.

And if you look around, you’ll see this playing out everywhere…

·       If you delay the gratification of watching television and get your homework done now, then you’ll learn more and get better grades.

·       If you delay the gratification of buying desserts and chips at the store, then you’ll eat healthier when you get home.

·       If you delay the gratification of finishing your workout early and put in a few more reps, then you’ll be stronger.

… and countless other examples.

Success usually comes down to choosing the pain of discipline over the ease of distraction. And that’s exactly what delayed gratification is all about. [1]

The premise is that if you have the willpower, discipline, or the mental aptitude to forestall your immediate desire to have gratification, the marshmallow, you are more likely to have more success later on in life. Having this ability demonstrates our willpower to put off the immediate rewards while dealing successfully with the unpleasant duties.

During our crisis moment in the COVID-19 pandemic, we are challenged just as those children were challenged. We are asked to put off immediate gratification of living our regular lives, or making our livings as we normally would. We are asked to not go shopping, go out to eat and drink, to put off everything we had considered normal as our society goes through the lockdown.

We were all able to comply because we understood the ramifications of letting the virus persist unchecked. We learned and believed in lowering the curve and to forestall the possible chaos that could come from a full-blown pandemic. We did it with some grumbling, but we all took it in stride because this pandemic is such a massive unknown. After months under a lockdown it is understandable that we all have a short fuse.  We are pretty much at our wit’s end and are itching to go out and lead our regular lives again, the return to normality that is the carrot at the end of the stick.

The undesired and unintended consequence that this lockdown has wrought is that it has essentially destroyed the economy.  36 million people out of work to date is catastrophic. Large and small businesses have been decimated; many have declared bankruptcy never to return again.

It is no wonder that we are all itching to get back to reclaim our society, our jobs, our economy, and our normal way of life. So much so that some have taken to the streets to protest what some consider to be draconian measures continuing the lockdown. In their haste to return to normality, I would guess that the vast majority did not considered or is ignoring the fact that the nationwide infection rate is still on the climb, ignoring the expert opinions  on everything, infection rate, the necessity of further isolation, the need for more significant testing, and the dangers of igniting the infections anew.

In essence, a number of people who has failed the marshmallow experiment. They cannot delay their gratification: their desire for returning  to a state of normality, so they chose to ignore the potential dangers with reopening before all the states has reached the CDC edict on the when reopening is safe and have taken it upon themselves to jump the gun.

Unfortunately, some of the chief executive, leaders, have failed the marshmallow experiment as well. As of the last weekend, 48 states have reopened their states to businesses, some with strict social distancing restrictions, and some with relatively lax rules. Only nine states meet the reopening criteria that the CDC recommends.

There is nothing that can be done at this point to reverse the reopening, unless a catastrophic second wave hits all the states and municipalities that have opened prematurely. As an individual, we can choose to not partake in the reopening until we are sure that the second wave has been averted. The unsatisfactory and nagging feeling is that  we, those who passed the marshmallow test, are at the mercy of those who did not, that our lives may be adversely affected by their inability to delay their gratification, and that those who passed the marshmallow experiment are not infected or worse because of the actions of those who did not pass the marshmallow experiment.

No basket of hot wings is worth sentencing your fellow human to extreme sickness or death.

Book Review-The Art of Statistics-Learning from Data By David Spiegelhalter

David Spiegelhalter is a world renowned statistician. He does research in statistics, he teaches statistics, and he also applies his statistical knowledge to real world problems. This makes him uniquely qualified to write a book that promises so much. The Art of Statistics: Learning from Data certainly promises a lot to the reader, it is an immense undertaking to say the least. It is a thick book but not as thick as I thought it would be, mainly because I hadn't counted on the book being so densely packed with knowledge and insight.

I had listened to a podcast interview with Spiegelhalter  while I was reading the book,  he says on this podcast that this is the book that this the culmination of a lifelong effort to best communicate the knowledge that he had accumulated in his time as a statistician, and he wanted to write a book out of which  he could teach the material when he's teaching statistics. He took the initiative of organizing the knowledge that he accrued and put it in an order as well as explained the material in the best way he felt was possible.

Early on in the book he talks about avoiding the mathematical tools and the grind of doing statistics in this book because there are plenty of other books that are focused on turning the crank and applying the mathematical tools,  so he didn't want to fill the book too much technical details. As concise, precise, and logical as he could be in writing, I think more mathematical examples would have helped. Not that he was bad at explaining the concepts, I felt like it would have been better to illustrate the ideas with some numerical examples. To be fair, he does have the copious amount of case studies in the book to help illustrate the concepts. They were all extremely interesting cases that applies statistics. Maybe it is just me, but I was always just slightly confused, I had to go back and reread and try to understand the main points. It is an extremely ambitious undertaking on his part to try to explain the art of statistics to a general audience, as most people are not well schooled in statistical thinking and mathematical computation. I am trained technically but I was not formally trained in probability and statistics. Whatever probability and statistics background I picked up was from my days as a practicing engineer, so my ability to understand what the author is trying to say is uneven, my shortfall altogether. Even though it was a struggle for me, I feel that it was a worthy struggle because I felt like he enlightened me and illuminated much of the intricate and complex concepts of statistics that I was not able to understand previously. I am using this book along with Ian Stewart's Does Dice Play God book as the fundamental basis of my own autodidactic attempt at learning probability and statistics. I am counting on these two books to give me a solid framework from which I can tackle the numerically intensive books that I have on probability and statistics. First a solid foundation, and then the nitty gritty details of the computation. I hope this works.

Spiegelhalter’s other purpose is that he is laying out a way  to do statistics better, to explain statistics better to the general public, and to eliminate much of the myths and sources of misunderstandings that people use statistics and the way people teach statistics. This is a Holy Grail which Spiegelhalter is trying to achieve through this book. The examples in the book demonstrates just how easily people can be misled applying statistics and then coming to the wrong conclusions.

He splits the book up into 14 chapters. The first three chapters talks about data what data mean in how the data is summarized and communicated. He delves deeply into the ideas of the distribution, the mean, and standard deviations, as well as the importance of data. Once he has explained the meaning and importance of data, he jumps straight into causality: what is causality and why is causality so important.  Causality is extremely important because this is where our human nature immediately go to when we use statistics.  We naturally and automatically jump to conclusions based upon our previous knowledge and we base our decisions and draw conclusions on what we think we see in the statistics without actually examining our logical fallacies and pitfalls from using statistics that probably does not point us into our conclusions.  Regression is next, and then he moves on to algorithms and analytics, tools we use to do prediction.

Following right behind the tools we use to make predictions, he naturally examines the role of  uncertainty: how certain are we about the statistics that we have taken and how can we determine how certain we are and how much confidence we have in the numbers that we have taken. Spiegelhalter also goes into great details about the data taking with importance of data taking: what the data taking tells us and how careless data taking can lead us into wrong conclusions. Probability is next, he calls it the language of uncertainty and variability, this is where many bog down in understanding the nuance of probability because it is very subtle and convoluted to understand. All this is used to set up the chapter where he puts probability and statistics together in context with one another. This is the big payoff because by putting the  two very difficult ideas together,  the resulting concept is much greater than the sum of the parts, the complications of the resultant increases exponentially. Spiegelhalter concedes that this is probably the most difficult chapter in this book to understand.

After having set that basis for the art of statistics, he answers the huge amount of questions that he had generated in his explanations.  One of the latter chapter is of particular interest because humans are very adept Bayesian thinkers, that is we tend to recalibrate our personal probabilities by taking into account any new information that we had obtained since we had come to a previous decision. Spiegelhalter jumps right into that with great gusto, and the chapter is fantastic reading. He distinguishes the way the Bayesians approach probability versus the frequentists. As a matter of fact, hat battle is still being fought today. He goes through the arguments for both cogently and his final assessment is that there are no absolutes in this regard, so that sometimes Bayesian is better and sometimes frequentists is better, better being more accurate and gives us better estimates while lowering the amount of uncertainty that we're dealing with. The trick then is the ability to discern which of the two ways of calculating probability is better.

In the end, Spiegelhalter makes a case for how we can practice the art of statistics better. This is the chapter where he lays out his manifesto on how statistics should be taught and how statistics should be used. You could feel the passion rise as you read the argument because this is obviously something that's very important to him, and he saved his best for last.

In addition, Spiegelhalter does a great service for the readers when is added,  at the end of each  chapter, a summary page of the conclusions that he draws from each chapter. He places them out  there in bullet form and it is identified by a black border around that page because he clearly want us to understand each and every point that he made during the body of the chapters. After having gone through all of the dense reading, it is helpful to have the bulleted list to help us recall what we had just read and try to make that learning permanent in our minds.

I would be lying if I said that this was an easy undertaking, it is not; but it is a very worthwhile hardship that I gladly undertook because of the amount of insight and potent argument that Spiegelhalter makes to my novice mind. His way of  not only communicating his knowledge that but also the way he exposes the weak arguments that he has found in his years of practicing statistics is masterful and convincing. The added bonus is his valuable insights on what would make the practice of statistics better, what would make it more useful to us. It is a a very hard read, but also a rewarding read.